Step Training-Dependent Plasticity in Spinal Cutaneous Pathways
Bibliographic record
Abstract
Plasticity after spinal cord injury can be initiated by specific patterns of sensory feedback, leading to a reorganization of spinal networks. For example, proprioceptive feedback from limb loading during the stance phase is crucial for the recovery of stepping in spinal-injured animals and humans. Our recent results showed that step training modified transmission from group I afferents of extensors in spinal cats. However, cutaneous afferents are also activated during locomotion and are necessary for proper foot placement in spinal cats. We therefore hypothesized that step training would also modify transmission in cutaneous pathways to facilitate recovery of stepping. We tested transmission in cutaneous pathways by comparing intracellular responses in lumbar motoneurons (n = 136) in trained (n = 11) and untrained (n = 7) cats spinalized 3-5 weeks before the acute electrophysiological experiment. Three cutaneous nerves were stimulated, and each evoked up to three motoneuronal responses mediated by at least three different pathways. Overall, of 71 cutaneous pathways tested, 10 were modified by step training: transmission was reduced in 7 and facilitated in 3. Remarkably, 6 of 10 involved the medial plantar nerve innervating the plantar surface of the foot, including two of the facilitated pathways. Because the cutaneous reflexes are exaggerated after spinalization, we interpret the decrease in most pathways as a normalization of cutaneous transmission necessary to recover locomotor movements. Overall, the results showed a high degree of specificity in plasticity among cutaneous pathways and indicate that transmission of skin inputs signaling ground contact, in particular, is modified by step training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".